Saskia Vola

After graduating in Comptational Linguistics she moved to Berlin and working as a NLP Java Developer she decided to become a full-time freelancer in 2014.
She works as a freelance consultant and developer in TextMining, NLP and is a big fan of ElasticSearch.
As a part-time digital nomad she traveled with communities like HackerParadise (http://www.hackerparadise.org/) and NomadCruise (http://www.nomadcruise.com/).
She recently started a platform for freelancers working in NLP/AI and TextMining called textminers.io

Welcome to Part 2 of How to use Elasticsearch for Natural Language Processing and Text Mining. It’s been some time since Part 1, so you might want to brush up on the basics before getting started. This time we’ll focus on one very important type of query for Text Mining.

ElasticSearch is a search engine and an analytics platform. But it offers many features that are useful for standard Natural Language Processing and Text Mining tasks. 1. Preprocessing (Normalization) Have you ever used the _analyze endpoint? As you know ElasticSearch has over 20 language-analyzers built in. What is an analyzer